Special Issue on Health Monitoring Technologies for Civil Infrastructure

In the past 20 years, a great number of novel and critical large-scale civil engineering structures have been constructed. These massive civil infrastructure projects pose big challenges and unique opportunities for engineers and researchers. Maintaining their safe and reliable operation is crucial in securing the wellbeing of people, protecting vast investments, and supporting the vitality of the economy. Consequently, installation of long-term and sophisticated structural health monitoring (SHM) systems on these key infrastructures have become a trend to monitor their loading conditions, assess their performance and condition, detect their damage, and guide their maintenance with the ultimate goals of ensuring the structures to function properly and safely during their entire lifecycle and preventing them from catastrophic failure under extreme events. Almost all of recently constructed infrastructures all around the world have been instrumented with different kinds of comprehensive SHM systems. For example, high-rise structures (Tianjin 117 Building, Canton Tower), long-span bridges (Aaizhai Bridge, Jiubao Bridge), long-span spatial structures (Shenzhen Vanke Center), deep buried tunnels (Xiamen Xiang’an subsea tunnel), and super-high-arch dams (Xiluodu Dam) are the types of large infrastructure subject to health monitoring as introduced and discussed in the papers in this special issue. The experience gained by research and practice in the design, installation, operation, maintenance, and upgrade of these SHM systems has promoted the applications of this technology and has a significant influence on the development of SHM systems around the world.

Given the significant amount of work involved globally and the unique feature of the SHM system and their applications, with an emphasis on civil, hydraulic and traffic engineering, this special issue on “Health Monitoring Technologies for Civil Infrastructure” in the Journal of Aerospace Engineering aims to provide a platform that allows active researchers to present the state-of-the-art and state-of-the-practice research and developments in technological innovation, development, and applications with SHM in large civil infrastructure and at the same time focus on issues facing engineers involved in construction and operations of civil infrastructure in challenging environments as they seek to achieve sustainable development on earth, in space, and on other planetary bodies. Their specific advances in these contributions are briefly summarized as follows.

In “Damage Identification and Optimal Sensor Placement for Structures under Unknown Traffic-Induced Vibrations,” Li et al. proposed a damage identification and optimal sensor placement approach for structures under unknown traffic-induced vibrations. Numerical studies on a seven-story planar frame structure are conducted to investigate the performance of the proposed approach. Identified results demonstrate that with the use of the identified optimal sensor locations, the simulated damage locations and severities in the structure can be identified efficiently when the measured responses are smeared with 5% noise effect.

In “Health Monitoring System Developed for Tianjin 117 High-Rise Building,” Liu et al. developed a SHM system for Tianjin 117 high-rise building, which is comprised of a reinforced-concrete core and perimeter steel framing that includes megacolumns and megabraces, with a height of 597 m. Moreover, in order to evaluate structural performance, an integrated structural monitor/analysis/evaluation (SMAE) software is also developed based on the Microsoft Windows platform. Since finite-element software called 3D3S was previously developed in the authors’ research group, combination of these two software packages will provide an integrated SHM platform for high-rise buildings as well as other long-span spatial structures.

In “Structural Performance Tracking to Multitype Members of Shenzhen Vanke Center in Construction Phase,” Lu et al. introduced the construction process of the Shenzhen Vanke Center, which has a remarkable structural form of four and five floors of the superstructure supported by giant tubes, and solid web thick walls and columns providing a large open space for the garden. Through analysis of the monitored measurements, the strain and stress measurements indicate that the key members are within the safety margin. The SHM system implemented during the construction phase for this complex structure not only provided the on-site experiments for the construction process but also proved the effectiveness of a structural health monitoring system in performance tracking of multitype members and safety control.

In “Early-Age Cracking in High-Performance Concrete Decks of a Curved Steel Girder Bridge,” Xia et al. investigated the factors inducing transverse cracks on a typical two-span curved continuous high-performance concrete bridge in New Jersey. Cracking issues due to effects of vertical temperature differentials, staging, shrinkage, settlement, tilt of piers, dead load, live load, etc., are quantitatively investigated and summarized through parametric study, which provides better understanding of the behavior of curved composite bridges.

In “Smart Monitoring of a Super High Arch Dam during the First Reservoir-Filling Phase,” Li et al. presented an in situ monitoring network of the Xiluodu Dam, including a deformation monitoring system using geodetic and nongeodetic monitoring. The monitoring result shows the positive correlation between the dam deformation and the reservoir water level. Good agreement is shown between the monitoring result and numerical data, demonstrating the correctness of the numerical method. The distribution characteristics of the crown cantilever during the reservoir-filling phase are presented based on the monitoring and numerical results, and the structural behavior of the dam in the future under normal operating loads is also predicted.

In “Field Application of an Innovative Bridge Scour Monitoring System with Fiber Bragg Grating Sensors,” Kong et al. proposed a scour monitoring system using fiber Bragg grating sensors. In order to demonstrate the effectiveness of this system, two test piles are installed in addition to two foundation piles of the field bridge, respectively, for long-term monitoring. The estimated scour depths based on the sensor data match qualitatively well with the actual scour depths observed on the bridge site and the concept of the scour monitoring design is well confirmed.

In “Stable Robust Extended Kalman Filter,” Mu et al. proposed a stable and robust filter for structural identification. This filter resolves the instability problems of the traditional extended Kalman filter (EKF). Instead of ad hoc assignment of the noise covariance matrices in the EKF, the proposed stable robust extended Kalman filter (SREKF) provides real-time updating of the noise parameters. This resolves the well-known instability problem of the EKF due to improper assignment of the noise covariance matrices.

In “Structural Monitoring Techniques for the Largest Excavation Section Subsea Tunnel:Xiamen Xiang’an Subsea Tunnel,” Zheng and Lei studied some techniques for monitoring the Xiamen Xiang’an subsea tunnel, the largest excavation section subsea tunnel. The layout of monitoring sections and selection of monitoring contents and locations are investigated according to both necessary numerical simulation and practical situation of the engineering project. The installed and verified SHM system can monitor both stress/deformation and corrosion status of reinforced concrete in the secondary supports of the tunnel, which can ensure the safety of operation of the tunnel, reduce the cost of maintenance, and improve the level of operation management of the subsea tunnel.

In “Structural Health Monitoring and Model Updating of Aizhai Suspension Bridge,” Yu and Ou developed a complete SHM system on the Aizhai Suspension Bridge, which consists of 112 sensors with different types, including four subsystems: automatic data collection subsystem, artificial maintenance management subsystem, early safety warning and comprehensive assessment subsystem, and centralized database management subsystem. A finite-element model updating method, which combines the substructure method with the response-surface model-updating method, is also proposed to reconstruct the actual working state of this suspension bridge in the early safety warning and comprehensive assessment subsystem.

In “Monitoring-Based Reliability Analysis of Aging Concrete Structures by Bayesian Updating,” Chen presented an effective approach for analyzing structural reliability during the service life of existing reinforced concrete structures on the basis of monitored information. The process of lifetime structural performance deterioration affected by reinforcement corrosion such as bond strength and load-bearing capacity is discussed. The results for a concrete bridge show that the proposed approach can provide reliable predictions of future structural performance and cost-effective maintenance strategy of aging reinforced concrete structures.

In “Sensor Fault Diagnosis for Structural Health Monitoring Based on Statistical Hypothesis Test and Missing Variable Approach,” Huang et al. presented a sensor fault-detection and isolation approach with application to structural health monitoring. Under the assumption that the measurement noise is Gaussian-distributed, a statistical hypothesis test model is established for the subsequent sensor fault-detection procedure, and a missing-variable approach is used to establish an isolation index to identify the specific faulty sensor. A benchmark structure developed for bridge health monitoring is adopted to validate and demonstrate the performance of the proposed method and the analysis results indicate that the method is effective in detecting and isolating both bias and drift sensor faults.

In “Research on Cracking of Reinforced Concrete Beam and Its Influence on Natural Frequency by Expanded Distinct Element Method,” Gao et al. proposed a distinct element model to simulate cracking in a reinforced-concrete beam. Numerical and experimental results show that at the crack-initiation stage, cracks appear below the loading points because of concentrated tensile stresses and grow upward. When these cracks penetrate three-quarters of the beam, cracks open in the bottom and gradually curve towards the loading points. Cracks caused by shear failure occur in the top of the beam. The beam fails because of a yielding of the longitudinal tensile reinforcement bar. Cracking and yielding of the longitudinal tensile reinforcement can be identified based on a continuously descending natural frequency.

In “Potential of Two Metaheuristic Optimization Tools for Damage Localization in Civil Structures,” Casciati and Elia introduced a highly nonlinear objective function that minimizes the discrepancies between the analytical and experimental features of a structure. Within a finite-element discretization, some stiffness parameters are chosen as reference variables. Two metaheuristic tools, the artificial bee colony algorithm and the firefly algorithm, are applied to proceed the iterations toward the global minima of the objective function. By comparison between the identified and the analytical stiffness matrices, the damage detection and localization are performed.

In “FOS-Based Prestress Force Monitoring and Temperature Effect Estimation in Unbonded Tendons of PSC Girders,” Huynh and Kim estimated the effect of temperature variation on prestress force monitoring by fiber Bragg grating sensors embedded in prestressing tendons of prestressed concrete girders. Experimental and analytical investigations on a lab-scaled PSC girder show a temperature effect on the prestress force, which can be analytically predicted if the thermal expansion coefficient of the PSC girder is well-modeled in the prediction model.

In “Innovative Design of a Health Monitoring System and Its Implementation in a Complicated Long-Span Arch Bridge,” Zhou et al. presented an innovative SHM system designed and permanently deployed on a complicated long-span arch bridge during its construction for the purpose of continuous performance monitoring throughout the bridge life span. The SHM system integrates several novel and practice-based concepts, i.e., lifecycle concerns for system design and implementation, newly designed high-performance sensors, flexible modularized hardware and software, multilevel data management, multiscale condition evaluation, and reliable warning strategies. More than 300 sensors of 10 types are installed on the bridge with the aim of monitoring the environmental effects and structural responses. Selected preliminary monitoring results are outlined, including structural temperatures, main girder deformations, relationship between structural temperature and main girder deformation, and dynamic responses.

In “Visualized Spatiotemporal Data Management System for Lifecycle Health Monitoring of Large-Scale Structures,” Ni et al. presented an effective visualized data management system specific for managing immense and heterogeneous SHM data by integrating nested relational database, three-dimensional (3D) model technology, and virtual reality technology. A custom nested data model is designed to store redundant inherent temporal data and hierarchical inherent spatial data. Strategies for speeding up querying massive data are set up in the database. Making use of OpenSceneGraph 3D engine, a 3D model is reconstructed from the 3D spatial data, which serves as a platform for data visualization. A four-dimensional (4D) animation protocol is presented by tying temporal data and construction schedule to the 3D model. The efficiency of the proposed data management system is exemplified through its application to a supertall structure instrumented with a sophisticated long-term SHM system.

In “Virtual Reference Approach for Dynamic Distributed Sensing of Damage in Large Structures,” Babanajad et al. introduced a reference-free damage detection method based on distributed monitoring of strains in large structural systems. The method employs dynamic distributed strain data to formulate its own virtual reference state for detection of defect locations. An experimental program is designed to investigate the feasibility of the proposed approach in detecting the locations of very small defects. Distributed strain measurements are compared with the defect opening displacements measured by FBG displacement sensors. The experimental results indicate that the proposed method is capable of detecting defects in the order of 50 μm in width.

In “Deformation Monitoring and Performance Analysis on the Shield Tunnel Influenced by Adjacent Deep Excavations,” Zhang et al. investigated health monitoring on the deformation of operating metro shield tunnels influenced by a close deep excavation with a case in Shanghai. Both manual and automatic measuring methods are adopted in the safety monitoring during deep excavation, and the results of both methods are compared to analyze the deviation of measuring methods. Comparison shows that the automatic monitoring results could be affected by poor working conditions and cumulative error. It is suggested that different monitoring methods should be verified by each other to ensure reliability and accuracy of the monitoring results.

In “Data Fusion Analysis Method for Assessment on Safety Monitoring Results of Deep Excavations,” Chen et al. presented a comprehensive assessment method to find anomalies in safety monitoring results. Data fusion analysis on both a single monitoring item and the correlation of multiple monitoring items are proposed and studied. The one class support vector machine (SVM) is used to improve the data fusion analysis between single monitoring item and different excavation parameters, and they are then developed to 3D fusion analysis on single item and multiparameter excavations. The mechanical and geometric patterns between different monitoring items are studied to propose a data fusion analysis on multiple monitoring items and then to build the assessment criteria. Based on these two kinds of data fusion analysis, the mass monitoring data can be analyzed completely to assess the safety state of deep excavations. An application in two cases of deep excavation in Shanghai shows that the proposed method is effective in data anomaly assessment.

In “Vibration and Deformation Monitoring of a Long-Span Rigid-Frame Bridge with Distributed Long-Gauge Sensors,” Zhang et al. presented the concept of a long-gauge fiber-optic sensor and its merit to reveal local and global structural features. Monitoring of a long-span bridge is performed using long-gauge sensors, and a method to calculate structural deformation distribution from the measured long-gauge strains is proposed, in which shear-deformation effect is considered because the main girder of the studied bridge is deep with a length–depth ratio of 17.9 at the end of the middle span. Modal identification of the studied bridge using the measured long-gauge dynamic strains is also performed, from which strain and displacement mode shapes are identified. Those results demonstrate the superiority of long-gauge fiber-optic sensors and their successful application to the studied bridge.